Computer audition (CA) has been demonstrated to be efficient in healthcare domains for speech-affecting disorders (e.g., autism spectrum, depression, or Parkinson's disease) and body sound-affecting abnormalities (e. g., abnormal bowel sounds, heart murmurs, or snore sounds). Nevertheless, CA has been underestimated in the considered data-driven technologies for fighting the COVID-19 pandemic caused by the SARS-CoV-2 coronavirus. In this light, summarise the most recent advances in CA for COVID-19 speech and/or sound analysis. While the milestones achieved are encouraging, there are yet not any solid conclusions that can be made. This comes mostly, as data is still sparse, often not sufficiently validated and lacking in systematic comparison with related diseases that affect the respiratory system. In particular, CA-based methods cannot be a standalone screening tool for SARS-CoV-2. We hope this brief overview can provide a good guidance and attract more attention from a broader artificial intelligence community.
翻译:事实证明,计算机试镜(CA)在语言影响障碍(如自闭症谱系、抑郁症或帕金森氏病)和身体声音影响异常(如不正常肠道声音、心脏杂音或鼻涕声)的保健领域是有效的。然而,在考虑数据驱动的技术中,CA被低估,以对抗SARS-COV-2 Corona病毒造成的COVID-19大流行。从这一点来看,总结CA的最新进展,以进行COVID-19演讲和/或声音分析。虽然已经取得的里程碑令人鼓舞,但还没有能够得出任何可靠的结论。这主要是因为数据仍然稀少,往往没有得到充分验证,而且缺乏与影响呼吸系统的有关疾病的系统比较。特别是,基于CAA的方法不能成为SAS-COV-2的独立筛选工具。我们希望这一简要概述能够提供良好的指导,吸引更广泛的人工智能界更多关注。